In the past few years, considerable progress has been made on learning-based methods for determining a classifier for object detection. In a learning based method, a set of training image samples, including object and non-object instances, are used to learn the classifier. A classifier uses a set of features to distinguish an object from a non-object. For example, Viola and Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR'01), Kauai, Hi., December 2001, use Adaboost learning to select features and learn a classifier from a large pool of simple, computationally efficient features.
Following Viola and Jones, different extensions of the Viola and Jones method have been proposed, both on the learning algorithm level and on the feature level. For example, on the learning algorithm level, GentleBoost, Real Adaboost, and Vector Boosting algorithms have been proposed to replace the Adaboost algorithm of Viola and Jones. On the feature level, an extended set of Haar-like features, edge orientation histograms, and a sparse feature set have been proposed to be used to learn a classifier. In addition, Principal Component Analysis and Fisher Subspace Analysis have been used as part of the feature pool.